A typical method to address the domain shift problem is aligning the two distributions by adversarial training [1]}, domain alignment [2]} and knowledge distillation [3]}. Besides, Chen \(et\ al\) . [4]} conducted experiments employing several popular vanilla FSL algorithms on the setting of CD-FSL, which demonstrates that the pre-trained and fine-tuned approaches could improve the performance on the unseen domain. Although these approaches achieve promising results, they applied labeled or unlabeled samples from the target domain before testing. Therefore, they break the CD-FSL setting that the target domain is absolutely unknown during the training.
